Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...
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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

R Data science includes data analysis. It is an important component of the skill set required for many jobs in this area. But it's not the only necessary skill. They play active roles in the design and implementation work of four related areas:

Data architecture

In data acquisition

Data analysis

In data archiving

b. What is Machine Learning?

Generally, there are 3 types of learning algorithm:

a. Supervised Machine Learning Algorithms

To make predictions we use this machine learning algorithm. Further, this algorithm searches for patterns within the value labels. That was assigned to data points.

b. Unsupervised Machine Learning Algorithms

No labels are associated with data points. Also, these machine learning algorithms organize the data into a group of clusters. Moreover, it needs to describe its structure. Also, to make complex data look simple and organized for analysis.

c. Reinforcement Machine Learning Algorithms

We use these algorithms to choose an action. Also, we can see that it is based on each data point. Moreover, after some time the algorithm changes its strategy to learn better. Also, achieve the best reward.

c. What is Deep Learning

As Machine learning focuses only on solving real-world problems. Also, it takes few ideas of artificial intelligence. Moreover, machine learning does through the neural networks. That are designed to mimic human decision-making capabilities.

Machine Learning tools and techniques are the two key narrow subsets. That only focuses more on deep learning. Furthermore, we need to apply it to solve any problem. That requires thought- human or artificial.

Any Deep neural network will consist of three types of layers:

The Input Layer

The Hidden Layer

The Output Layer

d. What is Artificial Intelligence

Basically, Artificial intelligence is a very broad term. Also, it is an attempt to make computers think like human beings. Moreover, any technique, code or algorithm that enables machines to develop. Also, behaviors falls under this category.

As we must be aware that an artificial intelligence system can be as simple as a software that plays chess. It doesn't matter how complex the system, artificial intelligence is only in its nascent stages.

3. How Does Data Science Relate to AI, ML & DL?

Data science is an inter-disciplinary field that has skills used in various fields such as statistics, machine learning, visualization, etc. It is general process and method that analyze and manipulate data. Also, enables to find meaning and appropriate information from large volumes of data. This makes it possible for us to use data for making key decisions in business, science, technology, and even politics.

4. Conclusion

As a result, we have briefly studied Data Science vs Artificial Intelligence vs Machine Learning vs Deep Learning. Also, we will learn clearly what every language is specified for. Furthermore, if you feel any query, feel free to ask in the comment section.